Design and Prepare a Machine Learning Solution
Start with the problem, not the algorithm
A well-designed solution begins by translating a business objective into a measurable ML objective. Ask what decision the model will influence, what the cost of a false positive versus a false negative is, and what metric reflects that trade-off. Candidates must know the difference between a business KPI (revenue saved, churn reduced) and a model metric (AUC, F1, RMSE), and must be able to justify why a chosen metric fits an imbalanced or asymmetric problem.
Data and architecture choices
Understand data-source assessment: volume, velocity, label availability, freshness, and privacy classification. Know when to use batch versus streaming ingestion, and how a feature store separates offline training features from online serving features to prevent training-serving skew. Be comfortable describing workspace assets: compute clusters, datastores, environments, and registered datasets with versioning.
Traps to watch for
- Choosing accuracy for a rare-event problem; prefer precision/recall or a cost-weighted metric.
- Confusing a validation split with a test set, or tuning on the test set.
- Ignoring leakage: target-derived features, future information, or duplicate rows across splits.
- Assuming more data always helps when labels are noisy or biased.
- Forgetting operational constraints (latency budget, cost ceiling, explainability requirement) at design time.
Worked rule of thumb
If a model must respond in under 100 ms and the feature requires a multi-second join, the design is wrong regardless of accuracy. Rule: fix the serving constraint first, then choose the simplest model family that meets it, and only then invest in complexity.
Design checklist
State the objective, the label definition, the metric and threshold policy, the data sources and their refresh cadence, the split strategy, and the acceptance criteria before any training begins.